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    Method, apparatus and computer program product for graph-based encoding of natural language data objects
    1.
    发明授权
    Method, apparatus and computer program product for graph-based encoding of natural language data objects 有权

    公开(公告)号：US12087413B2

    公开(公告)日：2024-09-10

    申请号：US17448292

    申请日：2021-09-21

    申请人： UnitedHealth Group Incorporated

    发明人： Irfan Bulu

    IPC分类号： G16H10/60 ,  G06F16/33 ,  G06F40/279 ,  G06T11/20

    CPC分类号： G16H10/60 ,  G06F16/3344 ,  G06F40/279 ,  G06T11/206

    摘要： Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include retrieving a plurality of natural language data objects from a database; determining, based at least in part on the plurality of natural language data objects and by utilizing an entity extraction machine learning model, a plurality of entity identifiers for the plurality of natural language data objects; determining, based at least in part on the plurality of entity identifiers and by utilizing the entity extraction machine learning model, one or more entity relationship identifiers for the plurality of natural language data objects; generating, based at least in part on the plurality of entity identifiers and the one or more entity relationship identifiers, a graph-based data object for the plurality of natural language data objects; and performing one or more prediction based actions based at least in part on the graph-based data object.

    SYSTEMS AND METHODS FOR TRAINING AND LEVERAGING A MULTI-HEADED MACHINE LEARNING MODEL FOR PREDICTIVE ACTIONS IN A COMPLEX PREDICTION DOMAIN
    2.
    发明公开
    SYSTEMS AND METHODS FOR TRAINING AND LEVERAGING A MULTI-HEADED MACHINE LEARNING MODEL FOR PREDICTIVE ACTIONS IN A COMPLEX PREDICTION DOMAIN 审中-公开

    公开(公告)号：US20240256988A1

    公开(公告)日：2024-08-01

    申请号：US18309092

    申请日：2023-04-28

    申请人： UnitedHealth Group Incorporated

    发明人： Carlos W. MORATO ,  Yan WANG ,  Mamatha JULURU

    IPC分类号： G06N20/20 ,  G06F40/30

    CPC分类号： G06N20/20 ,  G06F40/30

    摘要： Various embodiments of the present disclosure provide machine learning techniques for transforming third-party coding sets to universal canonical representations. The techniques may include receiving a plurality of training datasets corresponding to a plurality of predictive categories and generating a plurality of teacher models respectively corresponding to the plurality of predictive categories based on the plurality of training datasets. The techniques include generating a multi-headed composite model based on a plurality of trained parameters for each of the plurality of teacher models. The multi-headed composite model includes a plurality of model heads that respectively correspond to the plurality of teacher models and the plurality of predictive categories. The multi-headed composite model is leveraged to generate an output embedding for a text input of any predictive category. Each text input is processed by selecting a particular head of the multi-headed composite model that corresponds to the predictive category of the text input.

    MACHINE-LEARNING BASED TRANSCRIPT SUMMARIZATION
    4.
    发明公开
    MACHINE-LEARNING BASED TRANSCRIPT SUMMARIZATION 审中-公开

    公开(公告)号：US20230419051A1

    公开(公告)日：2023-12-28

    申请号：US17937616

    申请日：2022-10-03

    申请人： UnitedHealth Group Incorporated

    发明人： Rajesh SABAPATHY ,  Chirag MITTAL ,  Gourav AWASTHI ,  Aditya Teja JOSYULA

    IPC分类号： G06F40/56 ,  G06F40/35 ,  G06F40/247

    CPC分类号： G06F40/56 ,  G06F40/35 ,  G06F40/247

    摘要： There is a need for more effective and efficient predictive natural language summarization. This need is addressed by applying hybrid extractive and abstractive summarization techniques in a unique processing pipeline to generate a cohesive and comprehensive summary of a multi-party interaction. A method for generating the summary of a multi-party interaction includes receiving a multi-party interaction transcript data object comprising a plurality of interaction utterances from at least two participants; using an extractive summarization model to identify a key sentence of the multi-party interaction transcript data object; identifying an interaction utterance from the multi-party interaction transcript data object that corresponds to the key sentence; generating a contextual summary for the multi-party interaction transcript data object based at least in part on the interaction utterance; and generating a reported contextual summary for the multi-party interaction transcript data object based at least in part on the contextual summary.

    DATABASE MANAGEMENT SYSTEMS USING DISTRIBUTED DATABASE UPDATE MANAGEMENT OPERATIONS
    10.
    发明公开
    DATABASE MANAGEMENT SYSTEMS USING DISTRIBUTED DATABASE UPDATE MANAGEMENT OPERATIONS 审中-公开

    公开(公告)号：US20230281173A1

    公开(公告)日：2023-09-07

    申请号：US17683967

    申请日：2022-03-01

    申请人： UnitedHealth Group Incorporated

    发明人： Sudheer Jaisawal

    IPC分类号： G06F16/215 ,  G06F16/23 ,  G06F11/34

    CPC分类号： G06F16/215 ,  G06F16/2358 ,  G06F11/3409

    摘要： Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using distributed database update management techniques that utilize at least one of a field value temporal scoring machine learning model, total field utility measures, and distributed database update routines.